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Monitoring training status with HR measures: do all roads lead to Rome?

Buchheit M - Front Physiol (2014)

Bottom Line: For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load, and intensity distribution).The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker's sensitivity to changes in training status and the practical constrains required for the measurements.However, measures of heart rate cannot inform on all aspects of wellness, fatigue, and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status in athletes participating in aerobic-oriented sports.

View Article: PubMed Central - PubMed

Affiliation: Sport Science Department, Myorobie Association Montvalezan, France.

ABSTRACT
Measures of resting, exercise, and recovery heart rate are receiving increasing interest for monitoring fatigue, fitness and endurance performance responses, which has direct implications for adjusting training load (1) daily during specific training blocks and (2) throughout the competitive season. However, these measures are still not widely implemented to monitor athletes' responses to training load, probably because of apparent contradictory findings in the literature. In this review I contend that most of the contradictory findings are related to methodological inconsistencies and/or misinterpretation of the data rather than to limitations of heart rate measures to accurately inform on training status. I also provide evidence that measures derived from 5-min (almost daily) recordings of resting (indices capturing beat-to-beat changes in heart rate, reflecting cardiac parasympathetic activity) and submaximal exercise (30- to 60-s average) heart rate are likely the most useful monitoring tools. For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load, and intensity distribution). The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker's sensitivity to changes in training status and the practical constrains required for the measurements. However, measures of heart rate cannot inform on all aspects of wellness, fatigue, and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status in athletes participating in aerobic-oriented sports.

No MeSH data available.


Related in: MedlinePlus

Changes in the logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals measured after exercise (Ln rMSSD), submaximal exercise heart rate (HRex), counter movement jump height (CMJ) and training load (session-rate of perceived exertion load × training/match duration Impellizzeri et al., 2004) in three highly-trained young soccer players during a competitive training camp. The gray areas represent trivial changes (See Table 2) for HRex and CMJ (light gray) and HRV (dark gray). Errors bars (typical error of measurement, see Figure 7) have been omitted for clarity. While this might not be that clear when considering the actual spread of the TE, any change that is outside the gray areas is considered here as substantial (section Interpreting Changes in Heart Rate Measures: “Statistics are our weapons”). The changes in these variables in the three different players show different scenarios and illustrate how these indices can be used in combination to infer on training status and adaptations. Player A likely showed a positive adaptation to the camp (decrease in HRex and increase in Ln rMSSD), probably related to the fact that he arrived fresh and was a little bit detrained at the start following his previous injury. Playing at his position didn't require large high-intensity running demands (Buchheit et al., 2010c), so the balance between daily load and recovery was likely optimal for his fitness to improve (decrease in HRex) without compromising neuromuscular performance (CMJ remained stable). Player B, who was used to perform a very large amount of high-intensity actions during games as a striker, presented a stable fitness (HRex) and managed to maintain his ANS status into normal ranges, probably due to his very high fitness levels that may have allowed him to partially cope with the camp load (reduced relative intensity during games Mendez-Villanueva et al., 2013). However, neuromuscular fatigue progressively increased (decreased CMJ), consistent with the large playing demands. Finally, player C showed stable fitness and CMJ performance, but a clear decrease in HRV. He played the entire duration of games and in relation to his average fitness level, might not have completely coped with the load by the end of the camp. Nevertheless, the fact that his CMJ performance remained stable is consistent with the moderate neuromuscular demands of playing wide defender within his team's system of play (Buchheit et al., 2010c). MAS: maximal aerobic speed.
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Figure 6: Changes in the logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals measured after exercise (Ln rMSSD), submaximal exercise heart rate (HRex), counter movement jump height (CMJ) and training load (session-rate of perceived exertion load × training/match duration Impellizzeri et al., 2004) in three highly-trained young soccer players during a competitive training camp. The gray areas represent trivial changes (See Table 2) for HRex and CMJ (light gray) and HRV (dark gray). Errors bars (typical error of measurement, see Figure 7) have been omitted for clarity. While this might not be that clear when considering the actual spread of the TE, any change that is outside the gray areas is considered here as substantial (section Interpreting Changes in Heart Rate Measures: “Statistics are our weapons”). The changes in these variables in the three different players show different scenarios and illustrate how these indices can be used in combination to infer on training status and adaptations. Player A likely showed a positive adaptation to the camp (decrease in HRex and increase in Ln rMSSD), probably related to the fact that he arrived fresh and was a little bit detrained at the start following his previous injury. Playing at his position didn't require large high-intensity running demands (Buchheit et al., 2010c), so the balance between daily load and recovery was likely optimal for his fitness to improve (decrease in HRex) without compromising neuromuscular performance (CMJ remained stable). Player B, who was used to perform a very large amount of high-intensity actions during games as a striker, presented a stable fitness (HRex) and managed to maintain his ANS status into normal ranges, probably due to his very high fitness levels that may have allowed him to partially cope with the camp load (reduced relative intensity during games Mendez-Villanueva et al., 2013). However, neuromuscular fatigue progressively increased (decreased CMJ), consistent with the large playing demands. Finally, player C showed stable fitness and CMJ performance, but a clear decrease in HRV. He played the entire duration of games and in relation to his average fitness level, might not have completely coped with the load by the end of the camp. Nevertheless, the fact that his CMJ performance remained stable is consistent with the moderate neuromuscular demands of playing wide defender within his team's system of play (Buchheit et al., 2010c). MAS: maximal aerobic speed.

Mentions: Since the saturation phenomenon can confound the interpretation of training-induced adaptations, several approaches have been developed to prevent its occurrence. This includes, in comparison with the usual supine recordings, the use of sitting (Kiviniemi et al., 2007), standing (Buchheit et al., 2010a; Schmitt et al., 2013) or post-exercise (Buchheit et al., 2008, 2010a) measures, where there is a minimal level of sustained sympathetic activity. This directly constrains vagal activity below the “tipping point” of saturation (i.e., R-R interval >1000 ms Kiviniemi et al., 2004; Plews et al., 2012, 2013b). When using resting supine or seated measures as recommended for convenience (section One Simple Variable, Multiple Complex Indices), the only way to know whether the decrease in the vagal-related HRV indices is related more to sympathetic overactivity vs. saturation, is to examine the changes in those indices (e.g., rMSSD) with regard to concomitant changes in resting HR. Normalizing HRV data for the prevailing R-R interval is now also used in clinical setting (Billman, 2013). In practice with athletes, we recommend computing the Ln rMSSD/R-R ratio (Plews et al., 2012, 2013b) (Figure 5; Table 2). In the case of a sympathetic-mediated decrease in Ln rMSSD, the R-R intervals will likely be shortened (higher HR), maintaining or eventually increasing the ratio. While moderate increases might be optimal (increased “maximal sympathetic mobilization”), extreme increases in the ratio might reflect maladaptation to training, and in turn, reduced performance (Plews et al., 2013b). In the case of saturation, the R-R is increased (lower HR), and the ratio is substantially reduced (Plews et al., 2013b). Whether saturation is beneficial for performance is difficult to decipher, because each athlete likely displays his own Ln rMSSD/R-R ratio profile (Plews et al., 2013a). The Ln rMSSD/R-R ratio profile is also training-cycle dependent, which suggests that longitudinal monitoring over months/years is required to optimize the overall monitoring process for each athlete (Plews et al., 2013a). For an athlete showing a saturated profile during extensive training periods, a sudden loss of saturation would suggest either an increased readiness to perform (positive adaptation Plews et al., 2013b) or the apparition of fatigue (negative adaptation Borresen and Lambert, 2008; Bosquet et al., 2008b). To decipher between these two scenarios, practitioners may consider the magnitude of the increase in the Ln rMSSD/R-R ratio (see above), and additionally use psychometric measures such as perceived wellness (McLean et al., 2010; Buchheit et al., 2013a), and/or monitor neuromuscular performance via counter movement jumps for example (Cormack et al., 2008; McLean et al., 2010) (Figure 6). Finally, recent results have also suggested that within-athlete correlation between vagal-related HRV indices and RR intervals may be stronger in over-trained endurance athletes compared with controls (Kiviniemi et al., 2013). Further longitudinal studies in larger groups of athletes are required to confirm the potential of this measure.


Monitoring training status with HR measures: do all roads lead to Rome?

Buchheit M - Front Physiol (2014)

Changes in the logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals measured after exercise (Ln rMSSD), submaximal exercise heart rate (HRex), counter movement jump height (CMJ) and training load (session-rate of perceived exertion load × training/match duration Impellizzeri et al., 2004) in three highly-trained young soccer players during a competitive training camp. The gray areas represent trivial changes (See Table 2) for HRex and CMJ (light gray) and HRV (dark gray). Errors bars (typical error of measurement, see Figure 7) have been omitted for clarity. While this might not be that clear when considering the actual spread of the TE, any change that is outside the gray areas is considered here as substantial (section Interpreting Changes in Heart Rate Measures: “Statistics are our weapons”). The changes in these variables in the three different players show different scenarios and illustrate how these indices can be used in combination to infer on training status and adaptations. Player A likely showed a positive adaptation to the camp (decrease in HRex and increase in Ln rMSSD), probably related to the fact that he arrived fresh and was a little bit detrained at the start following his previous injury. Playing at his position didn't require large high-intensity running demands (Buchheit et al., 2010c), so the balance between daily load and recovery was likely optimal for his fitness to improve (decrease in HRex) without compromising neuromuscular performance (CMJ remained stable). Player B, who was used to perform a very large amount of high-intensity actions during games as a striker, presented a stable fitness (HRex) and managed to maintain his ANS status into normal ranges, probably due to his very high fitness levels that may have allowed him to partially cope with the camp load (reduced relative intensity during games Mendez-Villanueva et al., 2013). However, neuromuscular fatigue progressively increased (decreased CMJ), consistent with the large playing demands. Finally, player C showed stable fitness and CMJ performance, but a clear decrease in HRV. He played the entire duration of games and in relation to his average fitness level, might not have completely coped with the load by the end of the camp. Nevertheless, the fact that his CMJ performance remained stable is consistent with the moderate neuromuscular demands of playing wide defender within his team's system of play (Buchheit et al., 2010c). MAS: maximal aerobic speed.
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Related In: Results  -  Collection

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Figure 6: Changes in the logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals measured after exercise (Ln rMSSD), submaximal exercise heart rate (HRex), counter movement jump height (CMJ) and training load (session-rate of perceived exertion load × training/match duration Impellizzeri et al., 2004) in three highly-trained young soccer players during a competitive training camp. The gray areas represent trivial changes (See Table 2) for HRex and CMJ (light gray) and HRV (dark gray). Errors bars (typical error of measurement, see Figure 7) have been omitted for clarity. While this might not be that clear when considering the actual spread of the TE, any change that is outside the gray areas is considered here as substantial (section Interpreting Changes in Heart Rate Measures: “Statistics are our weapons”). The changes in these variables in the three different players show different scenarios and illustrate how these indices can be used in combination to infer on training status and adaptations. Player A likely showed a positive adaptation to the camp (decrease in HRex and increase in Ln rMSSD), probably related to the fact that he arrived fresh and was a little bit detrained at the start following his previous injury. Playing at his position didn't require large high-intensity running demands (Buchheit et al., 2010c), so the balance between daily load and recovery was likely optimal for his fitness to improve (decrease in HRex) without compromising neuromuscular performance (CMJ remained stable). Player B, who was used to perform a very large amount of high-intensity actions during games as a striker, presented a stable fitness (HRex) and managed to maintain his ANS status into normal ranges, probably due to his very high fitness levels that may have allowed him to partially cope with the camp load (reduced relative intensity during games Mendez-Villanueva et al., 2013). However, neuromuscular fatigue progressively increased (decreased CMJ), consistent with the large playing demands. Finally, player C showed stable fitness and CMJ performance, but a clear decrease in HRV. He played the entire duration of games and in relation to his average fitness level, might not have completely coped with the load by the end of the camp. Nevertheless, the fact that his CMJ performance remained stable is consistent with the moderate neuromuscular demands of playing wide defender within his team's system of play (Buchheit et al., 2010c). MAS: maximal aerobic speed.
Mentions: Since the saturation phenomenon can confound the interpretation of training-induced adaptations, several approaches have been developed to prevent its occurrence. This includes, in comparison with the usual supine recordings, the use of sitting (Kiviniemi et al., 2007), standing (Buchheit et al., 2010a; Schmitt et al., 2013) or post-exercise (Buchheit et al., 2008, 2010a) measures, where there is a minimal level of sustained sympathetic activity. This directly constrains vagal activity below the “tipping point” of saturation (i.e., R-R interval >1000 ms Kiviniemi et al., 2004; Plews et al., 2012, 2013b). When using resting supine or seated measures as recommended for convenience (section One Simple Variable, Multiple Complex Indices), the only way to know whether the decrease in the vagal-related HRV indices is related more to sympathetic overactivity vs. saturation, is to examine the changes in those indices (e.g., rMSSD) with regard to concomitant changes in resting HR. Normalizing HRV data for the prevailing R-R interval is now also used in clinical setting (Billman, 2013). In practice with athletes, we recommend computing the Ln rMSSD/R-R ratio (Plews et al., 2012, 2013b) (Figure 5; Table 2). In the case of a sympathetic-mediated decrease in Ln rMSSD, the R-R intervals will likely be shortened (higher HR), maintaining or eventually increasing the ratio. While moderate increases might be optimal (increased “maximal sympathetic mobilization”), extreme increases in the ratio might reflect maladaptation to training, and in turn, reduced performance (Plews et al., 2013b). In the case of saturation, the R-R is increased (lower HR), and the ratio is substantially reduced (Plews et al., 2013b). Whether saturation is beneficial for performance is difficult to decipher, because each athlete likely displays his own Ln rMSSD/R-R ratio profile (Plews et al., 2013a). The Ln rMSSD/R-R ratio profile is also training-cycle dependent, which suggests that longitudinal monitoring over months/years is required to optimize the overall monitoring process for each athlete (Plews et al., 2013a). For an athlete showing a saturated profile during extensive training periods, a sudden loss of saturation would suggest either an increased readiness to perform (positive adaptation Plews et al., 2013b) or the apparition of fatigue (negative adaptation Borresen and Lambert, 2008; Bosquet et al., 2008b). To decipher between these two scenarios, practitioners may consider the magnitude of the increase in the Ln rMSSD/R-R ratio (see above), and additionally use psychometric measures such as perceived wellness (McLean et al., 2010; Buchheit et al., 2013a), and/or monitor neuromuscular performance via counter movement jumps for example (Cormack et al., 2008; McLean et al., 2010) (Figure 6). Finally, recent results have also suggested that within-athlete correlation between vagal-related HRV indices and RR intervals may be stronger in over-trained endurance athletes compared with controls (Kiviniemi et al., 2013). Further longitudinal studies in larger groups of athletes are required to confirm the potential of this measure.

Bottom Line: For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load, and intensity distribution).The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker's sensitivity to changes in training status and the practical constrains required for the measurements.However, measures of heart rate cannot inform on all aspects of wellness, fatigue, and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status in athletes participating in aerobic-oriented sports.

View Article: PubMed Central - PubMed

Affiliation: Sport Science Department, Myorobie Association Montvalezan, France.

ABSTRACT
Measures of resting, exercise, and recovery heart rate are receiving increasing interest for monitoring fatigue, fitness and endurance performance responses, which has direct implications for adjusting training load (1) daily during specific training blocks and (2) throughout the competitive season. However, these measures are still not widely implemented to monitor athletes' responses to training load, probably because of apparent contradictory findings in the literature. In this review I contend that most of the contradictory findings are related to methodological inconsistencies and/or misinterpretation of the data rather than to limitations of heart rate measures to accurately inform on training status. I also provide evidence that measures derived from 5-min (almost daily) recordings of resting (indices capturing beat-to-beat changes in heart rate, reflecting cardiac parasympathetic activity) and submaximal exercise (30- to 60-s average) heart rate are likely the most useful monitoring tools. For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load, and intensity distribution). The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker's sensitivity to changes in training status and the practical constrains required for the measurements. However, measures of heart rate cannot inform on all aspects of wellness, fatigue, and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status in athletes participating in aerobic-oriented sports.

No MeSH data available.


Related in: MedlinePlus